Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations3509
Missing cells4114
Missing cells (%)6.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory466.0 KiB
Average record size in memory136.0 B

Variable types

Text2
Categorical5
Numeric9
DateTime1

Alerts

Area (SQM) is highly overall correlated with Floor Max and 2 other fieldsHigh correlation
District Name is highly overall correlated with Lease Years and 1 other fieldsHigh correlation
Floor Category is highly overall correlated with Floor Max and 3 other fieldsHigh correlation
Floor Max is highly overall correlated with Area (SQM) and 3 other fieldsHigh correlation
Floor Min is highly overall correlated with Area (SQM) and 3 other fieldsHigh correlation
Lease Years is highly overall correlated with District Name and 1 other fieldsHigh correlation
Postal District is highly overall correlated with District NameHigh correlation
Property Type is highly overall correlated with Floor Category and 1 other fieldsHigh correlation
Tenure Type is highly overall correlated with Lease YearsHigh correlation
Transacted Price ($) is highly overall correlated with Area (SQM) and 1 other fieldsHigh correlation
Type of Area is highly overall correlated with Floor Category and 3 other fieldsHigh correlation
Unit Price ($ PSM) is highly overall correlated with Transacted Price ($)High correlation
Project Name has 879 (25.0%) missing values Missing
Lease Years has 1453 (41.4%) missing values Missing
Floor Min has 891 (25.4%) missing values Missing
Floor Max has 891 (25.4%) missing values Missing
Area (SQM) is highly skewed (γ1 = 29.90420506) Skewed

Reproduction

Analysis started2024-11-22 10:53:22.224620
Analysis finished2024-11-22 10:53:25.580478
Duration3.36 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Project Name
Text

Missing 

Distinct309
Distinct (%)11.7%
Missing879
Missing (%)25.0%
Memory size54.8 KiB
2024-11-22T18:53:26.190973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length32
Median length26
Mean length14.512928
Min length3

Characters and Unicode

Total characters38169
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)4.0%

Sample

1st rowSUNSHINE PLAZA
2nd rowPAYA LEBAR SQUARE
3rd rowWOODS SQUARE
4th rowSUNSHINE PLAZA
5th rowINTERNATIONAL PLAZA
ValueCountFrequency (%)
plaza 403
 
6.6%
square 345
 
5.7%
the 306
 
5.0%
centre 293
 
4.8%
shopping 211
 
3.5%
tower 141
 
2.3%
paya 106
 
1.7%
lebar 106
 
1.7%
central 102
 
1.7%
peninsula 83
 
1.4%
Other values (365) 3992
65.6%
2024-11-22T18:53:26.850768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 4043
 
10.6%
3458
 
9.1%
A 3417
 
9.0%
N 2752
 
7.2%
T 2420
 
6.3%
O 2301
 
6.0%
R 2232
 
5.8%
L 2097
 
5.5%
I 2028
 
5.3%
S 2019
 
5.3%
Other values (29) 11402
29.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 34244
89.7%
Space Separator 3458
 
9.1%
Decimal Number 281
 
0.7%
Other Punctuation 181
 
0.5%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 4043
11.8%
A 3417
 
10.0%
N 2752
 
8.0%
T 2420
 
7.1%
O 2301
 
6.7%
R 2232
 
6.5%
L 2097
 
6.1%
I 2028
 
5.9%
S 2019
 
5.9%
P 1678
 
4.9%
Other values (16) 9257
27.0%
Decimal Number
ValueCountFrequency (%)
1 106
37.7%
2 62
22.1%
0 45
16.0%
5 24
 
8.5%
8 17
 
6.0%
3 13
 
4.6%
9 8
 
2.8%
7 6
 
2.1%
Other Punctuation
ValueCountFrequency (%)
@ 119
65.7%
' 53
29.3%
/ 9
 
5.0%
Space Separator
ValueCountFrequency (%)
3458
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34244
89.7%
Common 3925
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 4043
11.8%
A 3417
 
10.0%
N 2752
 
8.0%
T 2420
 
7.1%
O 2301
 
6.7%
R 2232
 
6.5%
L 2097
 
6.1%
I 2028
 
5.9%
S 2019
 
5.9%
P 1678
 
4.9%
Other values (16) 9257
27.0%
Common
ValueCountFrequency (%)
3458
88.1%
@ 119
 
3.0%
1 106
 
2.7%
2 62
 
1.6%
' 53
 
1.4%
0 45
 
1.1%
5 24
 
0.6%
8 17
 
0.4%
3 13
 
0.3%
/ 9
 
0.2%
Other values (3) 19
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 4043
 
10.6%
3458
 
9.1%
A 3417
 
9.0%
N 2752
 
7.2%
T 2420
 
6.3%
O 2301
 
6.0%
R 2232
 
5.8%
L 2097
 
5.5%
I 2028
 
5.3%
S 2019
 
5.3%
Other values (29) 11402
29.9%
Distinct307
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
2024-11-22T18:53:27.394218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length30
Median length24
Mean length13.896267
Min length9

Characters and Unicode

Total characters48762
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)2.8%

Sample

1st rowBENCOOLEN STREET
2nd rowPAYA LEBAR ROAD
3rd rowWOODLANDS SQUARE
4th rowBENCOOLEN STREET
5th rowANSON ROAD
ValueCountFrequency (%)
road 2177
25.9%
street 560
 
6.7%
jalan 211
 
2.5%
bridge 189
 
2.2%
upper 187
 
2.2%
north 140
 
1.7%
serangoon 127
 
1.5%
geylang 124
 
1.5%
cecil 113
 
1.3%
orchard 113
 
1.3%
Other values (346) 4470
53.1%
2024-11-22T18:53:27.876263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 5777
11.8%
R 5029
10.3%
4902
10.1%
O 4656
9.5%
E 4568
9.4%
D 3196
 
6.6%
N 2876
 
5.9%
T 2602
 
5.3%
S 2319
 
4.8%
L 1682
 
3.4%
Other values (31) 11155
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 43771
89.8%
Space Separator 4902
 
10.1%
Decimal Number 74
 
0.2%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 5777
13.2%
R 5029
11.5%
O 4656
10.6%
E 4568
10.4%
D 3196
 
7.3%
N 2876
 
6.6%
T 2602
 
5.9%
S 2319
 
5.3%
L 1682
 
3.8%
I 1577
 
3.6%
Other values (16) 9489
21.7%
Decimal Number
ValueCountFrequency (%)
1 23
31.1%
9 15
20.3%
3 10
13.5%
2 8
 
10.8%
7 7
 
9.5%
5 5
 
6.8%
6 3
 
4.1%
4 2
 
2.7%
8 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
' 4
40.0%
. 3
30.0%
/ 2
20.0%
\ 1
 
10.0%
Space Separator
ValueCountFrequency (%)
4902
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43771
89.8%
Common 4991
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 5777
13.2%
R 5029
11.5%
O 4656
10.6%
E 4568
10.4%
D 3196
 
7.3%
N 2876
 
6.6%
T 2602
 
5.9%
S 2319
 
5.3%
L 1682
 
3.8%
I 1577
 
3.6%
Other values (16) 9489
21.7%
Common
ValueCountFrequency (%)
4902
98.2%
1 23
 
0.5%
9 15
 
0.3%
3 10
 
0.2%
2 8
 
0.2%
7 7
 
0.1%
5 5
 
0.1%
- 5
 
0.1%
' 4
 
0.1%
. 3
 
0.1%
Other values (5) 9
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 5777
11.8%
R 5029
10.3%
4902
10.1%
O 4656
9.5%
E 4568
9.4%
D 3196
 
6.6%
N 2876
 
5.9%
T 2602
 
5.3%
S 2319
 
4.8%
L 1682
 
3.4%
Other values (31) 11155
22.9%

Property Type
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Office
1514 
Retail
1194 
Shop House
801 

Length

Max length10
Median length6
Mean length6.9130806
Min length6

Characters and Unicode

Total characters24258
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffice
2nd rowOffice
3rd rowOffice
4th rowOffice
5th rowOffice

Common Values

ValueCountFrequency (%)
Office 1514
43.1%
Retail 1194
34.0%
Shop House 801
22.8%

Length

2024-11-22T18:53:27.943702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-22T18:53:27.989494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
office 1514
35.1%
retail 1194
27.7%
shop 801
18.6%
house 801
18.6%

Most occurring characters

ValueCountFrequency (%)
e 3509
14.5%
f 3028
12.5%
i 2708
11.2%
o 1602
 
6.6%
O 1514
 
6.2%
c 1514
 
6.2%
a 1194
 
4.9%
l 1194
 
4.9%
t 1194
 
4.9%
R 1194
 
4.9%
Other values (7) 5607
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19147
78.9%
Uppercase Letter 4310
 
17.8%
Space Separator 801
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3509
18.3%
f 3028
15.8%
i 2708
14.1%
o 1602
8.4%
c 1514
7.9%
a 1194
 
6.2%
l 1194
 
6.2%
t 1194
 
6.2%
h 801
 
4.2%
p 801
 
4.2%
Other values (2) 1602
8.4%
Uppercase Letter
ValueCountFrequency (%)
O 1514
35.1%
R 1194
27.7%
S 801
18.6%
H 801
18.6%
Space Separator
ValueCountFrequency (%)
801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23457
96.7%
Common 801
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3509
15.0%
f 3028
12.9%
i 2708
11.5%
o 1602
 
6.8%
O 1514
 
6.5%
c 1514
 
6.5%
a 1194
 
5.1%
l 1194
 
5.1%
t 1194
 
5.1%
R 1194
 
5.1%
Other values (6) 4806
20.5%
Common
ValueCountFrequency (%)
801
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3509
14.5%
f 3028
12.5%
i 2708
11.2%
o 1602
 
6.6%
O 1514
 
6.2%
c 1514
 
6.2%
a 1194
 
4.9%
l 1194
 
4.9%
t 1194
 
4.9%
R 1194
 
4.9%
Other values (7) 5607
23.1%

Transacted Price ($)
Real number (ℝ)

High correlation 

Distinct1670
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7135491.9
Minimum120000
Maximum1.2814881 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2024-11-22T18:53:28.033111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum120000
5-th percentile428000
Q1950000
median1860000
Q34380000
95-th percentile16174680
Maximum1.2814881 × 109
Range1.2813681 × 109
Interquartile range (IQR)3430000

Descriptive statistics

Standard deviation44519649
Coefficient of variation (CV)6.2391844
Kurtosis470.72848
Mean7135491.9
Median Absolute Deviation (MAD)1162000
Skewness19.815452
Sum2.5038441 × 1010
Variance1.9819992 × 1015
MonotonicityNot monotonic
2024-11-22T18:53:28.083495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3500000 25
 
0.7%
1200000 25
 
0.7%
1300000 23
 
0.7%
1500000 22
 
0.6%
2000000 22
 
0.6%
900000 22
 
0.6%
1800000 21
 
0.6%
950000 21
 
0.6%
700000 21
 
0.6%
1100000 20
 
0.6%
Other values (1660) 3287
93.7%
ValueCountFrequency (%)
120000 1
 
< 0.1%
125000 1
 
< 0.1%
180000 2
0.1%
185000 1
 
< 0.1%
190000 1
 
< 0.1%
200000 3
0.1%
202888 1
 
< 0.1%
214888 1
 
< 0.1%
220000 1
 
< 0.1%
222888 1
 
< 0.1%
ValueCountFrequency (%)
1281488125 1
< 0.1%
1267511820 1
< 0.1%
810800000 1
< 0.1%
775000000 1
< 0.1%
700000000 1
< 0.1%
655000000 1
< 0.1%
538000000 1
< 0.1%
439000000 1
< 0.1%
340000000 1
< 0.1%
338000000 1
< 0.1%
Distinct61
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Minimum2019-10-01 00:00:00
Maximum2024-10-01 00:00:00
2024-11-22T18:53:28.129761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:28.178121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Type of Area
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Strata
2715 
Land
794 

Length

Max length6
Median length6
Mean length5.5474494
Min length4

Characters and Unicode

Total characters19466
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStrata
2nd rowStrata
3rd rowStrata
4th rowStrata
5th rowStrata

Common Values

ValueCountFrequency (%)
Strata 2715
77.4%
Land 794
 
22.6%

Length

2024-11-22T18:53:28.228602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-22T18:53:28.264586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
strata 2715
77.4%
land 794
 
22.6%

Most occurring characters

ValueCountFrequency (%)
a 6224
32.0%
t 5430
27.9%
S 2715
13.9%
r 2715
13.9%
L 794
 
4.1%
n 794
 
4.1%
d 794
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15957
82.0%
Uppercase Letter 3509
 
18.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6224
39.0%
t 5430
34.0%
r 2715
17.0%
n 794
 
5.0%
d 794
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
S 2715
77.4%
L 794
 
22.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 19466
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6224
32.0%
t 5430
27.9%
S 2715
13.9%
r 2715
13.9%
L 794
 
4.1%
n 794
 
4.1%
d 794
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6224
32.0%
t 5430
27.9%
S 2715
13.9%
r 2715
13.9%
L 794
 
4.1%
n 794
 
4.1%
d 794
 
4.1%

Area (SQM)
Real number (ℝ)

High correlation  Skewed 

Distinct904
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.72742
Minimum5
Maximum61511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2024-11-22T18:53:28.302997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile17
Q138
median70
Q3128.6
95-th percentile400.72
Maximum61511
Range61506
Interquartile range (IQR)90.6

Descriptive statistics

Standard deviation1607.4853
Coefficient of variation (CV)7.7384358
Kurtosis1051.2656
Mean207.72742
Median Absolute Deviation (MAD)39
Skewness29.904205
Sum728915.5
Variance2584008.9
MonotonicityNot monotonic
2024-11-22T18:53:28.349437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 60
 
1.7%
37 57
 
1.6%
52 55
 
1.6%
59 43
 
1.2%
60 42
 
1.2%
29 40
 
1.1%
47 40
 
1.1%
19 40
 
1.1%
48 40
 
1.1%
44 39
 
1.1%
Other values (894) 3053
87.0%
ValueCountFrequency (%)
5 2
 
0.1%
6 4
 
0.1%
7 3
 
0.1%
8 7
 
0.2%
9 10
0.3%
10 13
0.4%
11 12
0.3%
12 14
0.4%
13 20
0.6%
14 17
0.5%
ValueCountFrequency (%)
61511 1
< 0.1%
56673 1
< 0.1%
19976 1
< 0.1%
16673 1
< 0.1%
14006.1 1
< 0.1%
12898.2 1
< 0.1%
12096.9 1
< 0.1%
10369 1
< 0.1%
10257 1
< 0.1%
9807 1
< 0.1%

Unit Price ($ PSM)
Real number (ℝ)

High correlation 

Distinct2929
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34758.379
Minimum1801
Maximum1219955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2024-11-22T18:53:28.395976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1801
5-th percentile13522.2
Q120690
median27778
Q339583
95-th percentile79098.8
Maximum1219955
Range1218154
Interquartile range (IQR)18893

Descriptive statistics

Standard deviation31634.23
Coefficient of variation (CV)0.91011809
Kurtosis567.29496
Mean34758.379
Median Absolute Deviation (MAD)8402
Skewness16.56695
Sum1.2196715 × 108
Variance1.0007245 × 109
MonotonicityNot monotonic
2024-11-22T18:53:28.445234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 22
 
0.6%
25000 20
 
0.6%
50000 10
 
0.3%
30000 9
 
0.3%
15000 9
 
0.3%
26667 9
 
0.3%
16667 8
 
0.2%
24000 8
 
0.2%
37500 7
 
0.2%
27500 7
 
0.2%
Other values (2919) 3400
96.9%
ValueCountFrequency (%)
1801 1
< 0.1%
3431 1
< 0.1%
4242 1
< 0.1%
5495 1
< 0.1%
5542 1
< 0.1%
5764 1
< 0.1%
5765 1
< 0.1%
5927 1
< 0.1%
6054 1
< 0.1%
6064 1
< 0.1%
ValueCountFrequency (%)
1219955 1
< 0.1%
296406 1
< 0.1%
278735 1
< 0.1%
238273 1
< 0.1%
228972 1
< 0.1%
226415 1
< 0.1%
221277 1
< 0.1%
217941 1
< 0.1%
209953 1
< 0.1%
182215 1
< 0.1%

Postal District
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1567398
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2024-11-22T18:53:28.488133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median8
Q314
95-th percentile21
Maximum27
Range26
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.57107
Coefficient of variation (CV)0.71762113
Kurtosis-0.28445912
Mean9.1567398
Median Absolute Deviation (MAD)6
Skewness0.62435033
Sum32131
Variance43.17896
MonotonicityNot monotonic
2024-11-22T18:53:28.526717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 619
17.6%
7 433
12.3%
8 422
12.0%
14 377
10.7%
9 240
 
6.8%
15 232
 
6.6%
2 213
 
6.1%
6 161
 
4.6%
12 111
 
3.2%
21 109
 
3.1%
Other values (15) 592
16.9%
ValueCountFrequency (%)
1 619
17.6%
2 213
 
6.1%
3 99
 
2.8%
4 5
 
0.1%
5 36
 
1.0%
6 161
 
4.6%
7 433
12.3%
8 422
12.0%
9 240
 
6.8%
10 47
 
1.3%
ValueCountFrequency (%)
27 24
 
0.7%
26 7
 
0.2%
25 86
2.5%
23 11
 
0.3%
22 45
1.3%
21 109
3.1%
20 34
 
1.0%
19 105
3.0%
18 9
 
0.3%
16 35
 
1.0%

District Name
Categorical

High correlation 

Distinct25
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Raffles Place, Cecil, Marina, People's Park
619 
Middle Road, Golden Mile
433 
Little India
422 
Geylang, Eunos
377 
Orchard, Cairnhill, River Valley
240 
Other values (20)
1418 

Length

Max length52
Median length44
Mean length28.170989
Min length7

Characters and Unicode

Total characters98852
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Middle Road, Golden Mile
2nd row Geylang, Eunos
3rd row Kranji, Woodgrove
4th row Middle Road, Golden Mile
5th row Anson, Tanjong Pagar

Common Values

ValueCountFrequency (%)
Raffles Place, Cecil, Marina, People's Park 619
17.6%
Middle Road, Golden Mile 433
12.3%
Little India 422
12.0%
Geylang, Eunos 377
10.7%
Orchard, Cairnhill, River Valley 240
 
6.8%
Katong, Joo Chiat, Amber Road 232
 
6.6%
Anson, Tanjong Pagar 213
 
6.1%
High Street, Beach Road (part) 161
 
4.6%
Balestier, Toa Payoh, Serangoon 111
 
3.2%
Upper Bukit Timah, Clementi Park, Ulu Pandan 109
 
3.1%
Other values (15) 592
16.9%

Length

2024-11-22T18:53:28.568102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
road 873
 
6.2%
park 728
 
5.2%
raffles 619
 
4.4%
people's 619
 
4.4%
place 619
 
4.4%
marina 619
 
4.4%
cecil 619
 
4.4%
middle 433
 
3.1%
golden 433
 
3.1%
mile 433
 
3.1%
Other values (77) 8052
57.3%

Most occurring characters

ValueCountFrequency (%)
14047
14.2%
a 8993
 
9.1%
e 8095
 
8.2%
l 6232
 
6.3%
n 5728
 
5.8%
o 5338
 
5.4%
, 5324
 
5.4%
i 5102
 
5.2%
r 4275
 
4.3%
d 3382
 
3.4%
Other values (42) 32336
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64654
65.4%
Space Separator 14047
 
14.2%
Uppercase Letter 13886
 
14.0%
Other Punctuation 5943
 
6.0%
Close Punctuation 161
 
0.2%
Open Punctuation 161
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 2596
18.7%
R 1741
12.5%
M 1543
11.1%
C 1293
9.3%
G 951
 
6.8%
T 708
 
5.1%
B 636
 
4.6%
A 526
 
3.8%
E 472
 
3.4%
L 458
 
3.3%
Other values (14) 2962
21.3%
Lowercase Letter
ValueCountFrequency (%)
a 8993
13.9%
e 8095
12.5%
l 6232
9.6%
n 5728
8.9%
o 5338
8.3%
i 5102
7.9%
r 4275
 
6.6%
d 3382
 
5.2%
t 2523
 
3.9%
s 2345
 
3.6%
Other values (13) 12641
19.6%
Other Punctuation
ValueCountFrequency (%)
, 5324
89.6%
' 619
 
10.4%
Space Separator
ValueCountFrequency (%)
14047
100.0%
Close Punctuation
ValueCountFrequency (%)
) 161
100.0%
Open Punctuation
ValueCountFrequency (%)
( 161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78540
79.5%
Common 20312
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8993
 
11.5%
e 8095
 
10.3%
l 6232
 
7.9%
n 5728
 
7.3%
o 5338
 
6.8%
i 5102
 
6.5%
r 4275
 
5.4%
d 3382
 
4.3%
P 2596
 
3.3%
t 2523
 
3.2%
Other values (37) 26276
33.5%
Common
ValueCountFrequency (%)
14047
69.2%
, 5324
 
26.2%
' 619
 
3.0%
) 161
 
0.8%
( 161
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14047
14.2%
a 8993
 
9.1%
e 8095
 
8.2%
l 6232
 
6.3%
n 5728
 
5.8%
o 5338
 
5.4%
, 5324
 
5.4%
i 5102
 
5.2%
r 4275
 
4.3%
d 3382
 
3.4%
Other values (42) 32336
32.7%

Tenure Type
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Leasehold
2056 
Freehold
1452 
Unknown
 
1

Length

Max length9
Median length9
Mean length8.5856369
Min length7

Characters and Unicode

Total characters30127
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowLeasehold
2nd rowLeasehold
3rd rowLeasehold
4th rowLeasehold
5th rowLeasehold

Common Values

ValueCountFrequency (%)
Leasehold 2056
58.6%
Freehold 1452
41.4%
Unknown 1
 
< 0.1%

Length

2024-11-22T18:53:28.609569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-22T18:53:28.646849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
leasehold 2056
58.6%
freehold 1452
41.4%
unknown 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 7016
23.3%
o 3509
11.6%
h 3508
11.6%
l 3508
11.6%
d 3508
11.6%
L 2056
 
6.8%
a 2056
 
6.8%
s 2056
 
6.8%
F 1452
 
4.8%
r 1452
 
4.8%
Other values (4) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26618
88.4%
Uppercase Letter 3509
 
11.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7016
26.4%
o 3509
13.2%
h 3508
13.2%
l 3508
13.2%
d 3508
13.2%
a 2056
 
7.7%
s 2056
 
7.7%
r 1452
 
5.5%
n 3
 
< 0.1%
k 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
L 2056
58.6%
F 1452
41.4%
U 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 30127
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7016
23.3%
o 3509
11.6%
h 3508
11.6%
l 3508
11.6%
d 3508
11.6%
L 2056
 
6.8%
a 2056
 
6.8%
s 2056
 
6.8%
F 1452
 
4.8%
r 1452
 
4.8%
Other values (4) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7016
23.3%
o 3509
11.6%
h 3508
11.6%
l 3508
11.6%
d 3508
11.6%
L 2056
 
6.8%
a 2056
 
6.8%
s 2056
 
6.8%
F 1452
 
4.8%
r 1452
 
4.8%
Other values (4) 6
 
< 0.1%

Lease Years
Real number (ℝ)

High correlation  Missing 

Distinct14
Distinct (%)0.7%
Missing1453
Missing (%)41.4%
Infinite0
Infinite (%)0.0%
Mean325.48346
Minimum30
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2024-11-22T18:53:28.681114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile99
Q199
median99
Q399
95-th percentile999
Maximum9999
Range9969
Interquartile range (IQR)0

Descriptive statistics

Standard deviation793.72825
Coefficient of variation (CV)2.4386131
Kurtosis115.54157
Mean325.48346
Median Absolute Deviation (MAD)0
Skewness9.7985664
Sum669194
Variance630004.53
MonotonicityNot monotonic
2024-11-22T18:53:28.719192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
99 1599
45.6%
999 389
 
11.1%
63 24
 
0.7%
60 14
 
0.4%
9999 11
 
0.3%
956 5
 
0.1%
199 3
 
0.1%
103 2
 
0.1%
998 2
 
0.1%
98 2
 
0.1%
Other values (4) 5
 
0.1%
(Missing) 1453
41.4%
ValueCountFrequency (%)
30 1
 
< 0.1%
60 14
 
0.4%
63 24
 
0.7%
85 1
 
< 0.1%
95 1
 
< 0.1%
98 2
 
0.1%
99 1599
45.6%
103 2
 
0.1%
199 3
 
0.1%
956 5
 
0.1%
ValueCountFrequency (%)
9999 11
 
0.3%
999 389
 
11.1%
998 2
 
0.1%
978 2
 
0.1%
956 5
 
0.1%
199 3
 
0.1%
103 2
 
0.1%
99 1599
45.6%
98 2
 
0.1%
95 1
 
< 0.1%

Floor Min
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)0.4%
Missing891
Missing (%)25.4%
Infinite0
Infinite (%)0.0%
Mean4.8342246
Minimum-1
Maximum41
Zeros0
Zeros (%)0.0%
Negative121
Negative (%)3.4%
Memory size54.8 KiB
2024-11-22T18:53:28.753932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q36
95-th percentile21
Maximum41
Range42
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.6432671
Coefficient of variation (CV)1.3742156
Kurtosis4.8488122
Mean4.8342246
Median Absolute Deviation (MAD)0
Skewness2.0868377
Sum12656
Variance44.132997
MonotonicityNot monotonic
2024-11-22T18:53:28.787847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1502
42.8%
6 469
 
13.4%
11 249
 
7.1%
16 131
 
3.7%
-1 121
 
3.4%
21 86
 
2.5%
26 29
 
0.8%
36 15
 
0.4%
31 13
 
0.4%
41 3
 
0.1%
(Missing) 891
25.4%
ValueCountFrequency (%)
-1 121
 
3.4%
1 1502
42.8%
6 469
 
13.4%
11 249
 
7.1%
16 131
 
3.7%
21 86
 
2.5%
26 29
 
0.8%
31 13
 
0.4%
36 15
 
0.4%
41 3
 
0.1%
ValueCountFrequency (%)
41 3
 
0.1%
36 15
 
0.4%
31 13
 
0.4%
26 29
 
0.8%
21 86
 
2.5%
16 131
 
3.7%
11 249
 
7.1%
6 469
 
13.4%
1 1502
42.8%
-1 121
 
3.4%

Floor Max
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)0.4%
Missing891
Missing (%)25.4%
Infinite0
Infinite (%)0.0%
Mean8.4644767
Minimum-5
Maximum45
Zeros0
Zeros (%)0.0%
Negative121
Negative (%)3.4%
Memory size54.8 KiB
2024-11-22T18:53:28.820189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile5
Q15
median5
Q310
95-th percentile25
Maximum45
Range50
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.1604035
Coefficient of variation (CV)0.84593575
Kurtosis3.7367819
Mean8.4644767
Median Absolute Deviation (MAD)0
Skewness1.5130535
Sum22160
Variance51.271378
MonotonicityNot monotonic
2024-11-22T18:53:28.854591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 1502
42.8%
10 469
 
13.4%
15 249
 
7.1%
20 131
 
3.7%
-5 121
 
3.4%
25 86
 
2.5%
30 29
 
0.8%
40 15
 
0.4%
35 13
 
0.4%
45 3
 
0.1%
(Missing) 891
25.4%
ValueCountFrequency (%)
-5 121
 
3.4%
5 1502
42.8%
10 469
 
13.4%
15 249
 
7.1%
20 131
 
3.7%
25 86
 
2.5%
30 29
 
0.8%
35 13
 
0.4%
40 15
 
0.4%
45 3
 
0.1%
ValueCountFrequency (%)
45 3
 
0.1%
40 15
 
0.4%
35 13
 
0.4%
30 29
 
0.8%
25 86
 
2.5%
20 131
 
3.7%
15 249
 
7.1%
10 469
 
13.4%
5 1502
42.8%
-5 121
 
3.4%

Floor Category
Categorical

High correlation 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
01-05
1502 
Unknown
891 
06-10
469 
11-15
249 
21+
 
146
Other values (2)
252 

Length

Max length8
Median length5
Mean length5.5280707
Min length3

Characters and Unicode

Total characters19398
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01-05
2nd row01-05
3rd row06-10
4th row06-10
5th row16-20

Common Values

ValueCountFrequency (%)
01-05 1502
42.8%
Unknown 891
25.4%
06-10 469
 
13.4%
11-15 249
 
7.1%
21+ 146
 
4.2%
16-20 131
 
3.7%
Basement 121
 
3.4%

Length

2024-11-22T18:53:28.892501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-22T18:53:28.930797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
01-05 1502
42.8%
unknown 891
25.4%
06-10 469
 
13.4%
11-15 249
 
7.1%
21 146
 
4.2%
16-20 131
 
3.7%
basement 121
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 4073
21.0%
1 2995
15.4%
n 2794
14.4%
- 2351
12.1%
5 1751
9.0%
U 891
 
4.6%
k 891
 
4.6%
o 891
 
4.6%
w 891
 
4.6%
6 600
 
3.1%
Other values (8) 1270
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9696
50.0%
Lowercase Letter 6193
31.9%
Dash Punctuation 2351
 
12.1%
Uppercase Letter 1012
 
5.2%
Math Symbol 146
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2794
45.1%
k 891
 
14.4%
o 891
 
14.4%
w 891
 
14.4%
e 242
 
3.9%
a 121
 
2.0%
s 121
 
2.0%
m 121
 
2.0%
t 121
 
2.0%
Decimal Number
ValueCountFrequency (%)
0 4073
42.0%
1 2995
30.9%
5 1751
18.1%
6 600
 
6.2%
2 277
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
U 891
88.0%
B 121
 
12.0%
Dash Punctuation
ValueCountFrequency (%)
- 2351
100.0%
Math Symbol
ValueCountFrequency (%)
+ 146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12193
62.9%
Latin 7205
37.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2794
38.8%
U 891
 
12.4%
k 891
 
12.4%
o 891
 
12.4%
w 891
 
12.4%
e 242
 
3.4%
B 121
 
1.7%
a 121
 
1.7%
s 121
 
1.7%
m 121
 
1.7%
Common
ValueCountFrequency (%)
0 4073
33.4%
1 2995
24.6%
- 2351
19.3%
5 1751
14.4%
6 600
 
4.9%
2 277
 
2.3%
+ 146
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4073
21.0%
1 2995
15.4%
n 2794
14.4%
- 2351
12.1%
5 1751
9.0%
U 891
 
4.6%
k 891
 
4.6%
o 891
 
4.6%
w 891
 
4.6%
6 600
 
3.1%
Other values (8) 1270
 
6.5%

Transaction Year
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.815
Minimum2019
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-11-22T18:53:28.969125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2019
5-th percentile2020
Q12021
median2022
Q32023
95-th percentile2024
Maximum2024
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3978895
Coefficient of variation (CV)0.00069140326
Kurtosis-0.88557388
Mean2021.815
Median Absolute Deviation (MAD)1
Skewness-0.06361689
Sum7094549
Variance1.9540951
MonotonicityNot monotonic
2024-11-22T18:53:29.003967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 885
25.2%
2022 763
21.7%
2023 713
20.3%
2020 497
14.2%
2024 494
14.1%
2019 157
 
4.5%
ValueCountFrequency (%)
2019 157
 
4.5%
2020 497
14.2%
2021 885
25.2%
2022 763
21.7%
2023 713
20.3%
2024 494
14.1%
ValueCountFrequency (%)
2024 494
14.1%
2023 713
20.3%
2022 763
21.7%
2021 885
25.2%
2020 497
14.2%
2019 157
 
4.5%

Transaction Month
Real number (ℝ)

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6164149
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2024-11-22T18:53:29.037807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4116013
Coefficient of variation (CV)0.51562687
Kurtosis-1.2072962
Mean6.6164149
Median Absolute Deviation (MAD)3
Skewness-0.017217724
Sum23217
Variance11.639024
MonotonicityNot monotonic
2024-11-22T18:53:29.073762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 354
10.1%
6 327
9.3%
3 323
9.2%
4 316
9.0%
12 298
8.5%
7 295
8.4%
11 292
8.3%
5 273
7.8%
8 271
7.7%
9 262
7.5%
Other values (2) 498
14.2%
ValueCountFrequency (%)
1 258
7.4%
2 240
6.8%
3 323
9.2%
4 316
9.0%
5 273
7.8%
6 327
9.3%
7 295
8.4%
8 271
7.7%
9 262
7.5%
10 354
10.1%
ValueCountFrequency (%)
12 298
8.5%
11 292
8.3%
10 354
10.1%
9 262
7.5%
8 271
7.7%
7 295
8.4%
6 327
9.3%
5 273
7.8%
4 316
9.0%
3 323
9.2%

Interactions

2024-11-22T18:53:25.004647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.585405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.893882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.250219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.552313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.842741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.136672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.421609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.705455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:25.040667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.624273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.929275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.284990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.585789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.876900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.170642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.455782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.740665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:25.074445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.658969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.962409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.319958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.619381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.910598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.202724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.486729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.774800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:25.108876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.694006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.998065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.354150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.653503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.944375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.236091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.520503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.810762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:25.140790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.728228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.030617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.386952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.685628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.977344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.266778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.551393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.843583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:25.173448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.762127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.063866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.421416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.717324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.008879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.298725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.583387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.876691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:25.204687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.794518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.094270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.453061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.748383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.039687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.327892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.612574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.908192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:25.235395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.826441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.124566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.485097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.778377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.071087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.358245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.641996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.939144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:25.268682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:22.860933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.217556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.518506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:23.810383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.103820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.389843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.674657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-22T18:53:24.972313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-22T18:53:29.106144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Area (SQM)District NameFloor CategoryFloor MaxFloor MinLease YearsPostal DistrictProperty TypeTenure TypeTransacted Price ($)Transaction MonthTransaction YearType of AreaUnit Price ($ PSM)
Area (SQM)1.0000.1280.0000.5200.5200.036-0.1620.0200.0000.8770.047-0.0600.0360.076
District Name0.1281.0000.2980.2320.2320.5340.9980.4260.4360.0100.0400.0770.4380.055
Floor Category0.0000.2981.0000.9990.9990.0530.2350.7840.2390.0230.0350.0840.9260.092
Floor Max0.5200.2320.9991.0001.000-0.128-0.3640.4330.2730.4440.058-0.0131.000-0.018
Floor Min0.5200.2320.9991.0001.000-0.128-0.3640.4330.2730.4440.058-0.0131.000-0.018
Lease Years0.0360.5340.053-0.128-0.1281.0000.0010.0571.0000.167-0.000-0.0530.0000.329
Postal District-0.1620.9980.235-0.364-0.3640.0011.0000.3110.324-0.234-0.0110.0430.280-0.186
Property Type0.0200.4260.7840.4330.4330.0570.3111.0000.2050.0000.0060.0920.9490.081
Tenure Type0.0000.4360.2390.2730.2731.0000.3240.2051.0000.0000.0410.0290.2230.000
Transacted Price ($)0.8770.0100.0230.4440.4440.167-0.2340.0000.0001.0000.050-0.0280.0600.502
Transaction Month0.0470.0400.0350.0580.058-0.000-0.0110.0060.0410.0501.000-0.2240.0000.009
Transaction Year-0.0600.0770.084-0.013-0.013-0.0530.0430.0920.029-0.028-0.2241.0000.1360.059
Type of Area0.0360.4380.9261.0001.0000.0000.2800.9490.2230.0600.0000.1361.0000.163
Unit Price ($ PSM)0.0760.0550.092-0.018-0.0180.329-0.1860.0810.0000.5020.0090.0590.1631.000

Missing values

2024-11-22T18:53:25.396383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-22T18:53:25.482124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-22T18:53:25.556350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Project NameStreet NameProperty TypeTransacted Price ($)Sale DateType of AreaArea (SQM)Unit Price ($ PSM)Postal DistrictDistrict NameTenure TypeLease YearsFloor MinFloor MaxFloor CategoryTransaction YearTransaction Month
0SUNSHINE PLAZABENCOOLEN STREETOffice8500002024-10-01Strata45188897Middle Road, Golden MileLeasehold991501-05202410
1PAYA LEBAR SQUAREPAYA LEBAR ROADOffice23180002024-10-01Strata992341414Geylang, EunosLeasehold991501-05202410
2WOODS SQUAREWOODLANDS SQUAREOffice12300002024-10-01Strata522365425Kranji, WoodgroveLeasehold9961006-10202410
3SUNSHINE PLAZABENCOOLEN STREETOffice11200002024-10-01Strata59189837Middle Road, Golden MileLeasehold9961006-10202410
4INTERNATIONAL PLAZAANSON ROADOffice17635802024-10-01Strata90195952Anson, Tanjong PagarLeasehold99162016-20202410
5SUNSHINE PLAZABENCOOLEN STREETOffice11500002024-10-01Strata62185487Middle Road, Golden MileLeasehold991501-05202410
6111 SOMERSETSOMERSET ROADOffice22000002024-10-01Strata73301379Orchard, Cairnhill, River ValleyLeasehold99111511-15202410
7SOUTHBANKNORTH BRIDGE ROADOffice16888882024-10-01Strata94179677Middle Road, Golden MileLeasehold99162016-20202410
8INTERNATIONAL PLAZAANSON ROADOffice35000002024-10-01Strata219159822Anson, Tanjong PagarLeasehold99111511-15202410
9SUNSHINE PLAZABENCOOLEN STREETOffice12800002024-10-01Strata68188247Middle Road, Golden MileLeasehold991501-05202410
Project NameStreet NameProperty TypeTransacted Price ($)Sale DateType of AreaArea (SQM)Unit Price ($ PSM)Postal DistrictDistrict NameTenure TypeLease YearsFloor MinFloor MaxFloor CategoryTransaction YearTransaction Month
3546<NA>HONGKONG STREETShop House71000002019-11-01Land155457771Raffles Place, Cecil, Marina, People's ParkLeasehold99<NA><NA>Unknown201911
3547<NA>JOO CHIAT ROADShop House232000002019-11-01Land9732385415Katong, Joo Chiat, Amber RoadFreeholdNaN<NA><NA>Unknown201911
3548<NA>JALAN BESARShop House53000002019-11-01Land137387148Little IndiaFreeholdNaN<NA><NA>Unknown201911
3549<NA>NORRIS ROADShop House29500002019-11-01Land91323118Little IndiaFreeholdNaN<NA><NA>Unknown201911
3550<NA>DICKSON ROADShop House30000002019-11-01Land132227968Little IndiaLeasehold99<NA><NA>Unknown201911
3551<NA>JOO CHIAT PLACEShop House80000002019-11-01Land3312415515Katong, Joo Chiat, Amber RoadFreeholdNaN<NA><NA>Unknown201911
3552<NA>OUTRAM ROADShop House51800002019-10-01Land142364793Queenstown, Tiong BahruFreeholdNaN<NA><NA>Unknown201910
3553<NA>PAGODA STREETShop House162500002019-10-01Land1221335251Raffles Place, Cecil, Marina, People's ParkFreeholdNaN<NA><NA>Unknown201910
3554<NA>SMITH STREETShop House70000002019-10-01Land100700001Raffles Place, Cecil, Marina, People's ParkLeasehold999<NA><NA>Unknown201910
3555<NA>KILLINEY ROADShop House60500002019-10-01Land105574559Orchard, Cairnhill, River ValleyFreeholdNaN<NA><NA>Unknown201910